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Record W3173882469 · doi:10.1177/07308884211024711

Über-Alienated: Powerless and Alone in the Gig Economy

2021· article· en· W3173882469 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueWork and Occupations · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsUniversity of CalgaryUniversity of TorontoMcMaster University
Fundersnot available
KeywordsAlienationLonelinessIsolation (microbiology)AutonomyGig economySocial isolationSociologySocial psychologyWork (physics)GossipPsychologyPolitical scienceGender studiesEngineeringLaw

Abstract

fetched live from OpenAlex

While the gig economy has expanded rapidly in the last decade, few have studied the psychological ramifications of working for an online labor platform. Guided by classical and modern theories of work and alienation, we investigate whether engagement in platform work is associated with an increased sense of powerlessness and isolation. We analyze data from two national surveys of workers from the Canadian Quality of Work and Economic Life Study in September 2019 ( N = 2,460) and March 2020 ( N = 2,469). Analyses reveal greater levels of powerlessness and loneliness among platform workers—a pattern that is not fully explained by their higher levels of financial strain. Additional analyses of platform activity reveal that rideshare driving is more strongly associated with powerlessness and isolation than engagement in online crowdwork. We interpret our findings in light of platform firms’ use of algorithmic control and distancing strategies that may undermine worker autonomy and social connection.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.268
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it